Return to Main LECTURE 13: CEPSTRAL ANALYSIS Objectives ● Objectives: The Real Cepstrum: Homomorphic ❍ Introduce homomorphic transformations Definitions ❍ Understand the real cepstrum Pole-Zero Linear Prediction ❍ Introduce alternate ways to compute the cepstrum Applications: ❍ Explain how we compute mel-frequency "cepstrum" coefficients Vowel Cepstrum Source-Filter This lecture combines material from the course textbook: Frequency Warping Mel-Frequency X. Huang, A. Acero, and H.W. Hon, Spoken Language Processing - A Guide to Liftering Theory, Algorithm, and System Development, Prentice Hall, Upper Saddle River, New Jersey, USA, ISBN: 0-13-022616-5, 2001. Summary: Signal Modeling and information found in most standard DSP or speech textbooks: Typical Front End On-Line Resources: J. Deller, et. al., Discrete-Time Processing of Speech Signals, MacMillan Publishing Cepstrum Co., ISBN: 0-7803-5386-2, 2000. Hunt, ASRU'99 (pdf) Hunt, ASRU'99 (ppt) Ben Gold Oral History Return to Main Introduction: 01: Organization (html, pdf) Speech Signals: ECE 8463: FUNDAMENTALS OF SPEECH 02: Production (html, pdf) RECOGNITION 03: Digital Models Professor Joseph Picone (html, pdf) Department of Electrical and Computer Engineering Mississippi State University 04: Perception (html, pdf) email: [email protected] phone/fax: 601-325-3149; office: 413 Simrall 05: Masking URL: http://www.isip.msstate.edu/resources/courses/ece_8463 (html, pdf) Modern speech understanding systems merge interdisciplinary technologies from Signal Processing, 06: Phonetics and Phonology Pattern Recognition, Natural Language, and Linguistics into a unified statistical framework. These (html, pdf) systems, which have applications in a wide range of signal processing problems, represent a revolution in Digital Signal Processing (DSP). Once a field dominated by vector-oriented processors and linear 07: Syntax and Semantics algebra-based mathematics, the current generation of DSP-based systems rely on sophisticated statistical (html, pdf) models implemented using a complex software paradigm. Such systems are now capable of understanding continuous speech input for vocabularies of hundreds of thousands of words in Signal Processing: operational environments. 08: Sampling In this course, we will explore the core components of modern statistically-based speech recognition (html, pdf) systems. We will view speech recognition problem in terms of three tasks: signal modeling, network searching, and language understanding. We will conclude our discussion with an overview of state-of- 09: Resampling the-art systems, and a review of available resources to support further research and technology (html, pdf) development. 10: Acoustic Transducers Tar files containing a compilation of all the notes are available. However, these files are large and will (html, pdf) require a substantial amount of time to download. A tar file of the html version of the notes is available here. These were generated using wget: 11: Temporal Analysis (html, pdf) wget -np -k -m http://www.isip.msstate.edu/publications/courses/ece_8463/lectures/current 12: Frequency Domain Analysis (html, pdf) A pdf file containing the entire set of lecture notes is available here. These were generated using Adobe Acrobat. 13: Cepstral Analysis (html, pdf) Questions or comments about the material presented here can be directed to [email protected]. 14: Exam No. 1 (html, pdf) 15: Linear Prediction (html, pdf) 16: LP-Based Representations (html, pdf) Parameterization: 17: Differentiation (html, pdf) 18: Principal Components (html, pdf) 19: Linear Discriminant Analysis (html, pdf) LECTURE 13: CEPSTRAL ANALYSIS ● Objectives: ❍ Introduce homomorphic transformations ❍ Understand the real cepstrum ❍ Introduce alternate ways to compute the cepstrum ❍ Explain how we compute mel-frequency "cepstrum" coefficients This lecture combines material from the course textbook: X. Huang, A. Acero, and H.W. Hon, Spoken Language Processing - A Guide to Theory, Algorithm, and System Development, Prentice Hall, Upper Saddle River, New Jersey, USA, ISBN: 0-13-022616-5, 2001. and information found in most standard DSP or speech textbooks: J. Deller, et. al., Discrete-Time Processing of Speech Signals, MacMillan Publishing Co., ISBN: 0-7803-5386-2, 2000. HOMOMORPHIC TRANSFORMATIONS A homomorphic transformation converts a convolution into a sum: Consider the problem of recovering a filter's response from a periodic signal (such as a voiced excitation): The filter response can be recovered if we can separate the output of the homomorphic transformation using a simple filter: Note that the process of separating the signals is essentially a windowing processing. Is this useful for speech processing? THE REAL AND COMPLEX CEPSTRUM The real cepstrum of a digital signal x(n) is defined as: and the complex cepstrum is defined as: where the complex logarithm is used: Note that the real cepstrum, c(n), is the even part of complex cepstrum: The word cepstrum was coined by reversing the first syllable in the word spectrum. The cepstrum exists in a domain referred to as quefrency (reversal of the first syllable in frequency) which has units of time. THE CEPSTRUM OF POLE-ZERO FILTERS Consider a minimum phase system with a rational transfer function: Taking the complex logarithm: Taking an inverse z-transform: It is easy to see that the cepstrum is a decaying function of time (compact). Why is this desirable? Recalling that the real cepstrum can be computed from the even part of the complex cepstrum, the complex cepstrum can also be easily determined from the real cepstrum, c(n), as follows: LINEAR PREDICTION AND THE CEPSTRUM Consider an all-pole filter: The cepstrum can be determined by the following recursion: Note that if there are a finite number of filter coefficients, there are still an infinite number of cepstral coefficients. However, the series decays to zero and can be truncated. The proof of this result is shown below for completeness: AN EXAMPLE OF THE CEPSTRUM FOR A VOWEL Below is an example (from Noll) that demonstrates a typical cepstrum sequence for a vowel. The cepstrum is computed every 10 msec. From this example, we can see two important things: ● At the onset of the vowel, where the signal is not quite periodic, the peak in the cepstrum at the fundamental frequency is not well-formed. The amplitude of this peak grows as the signal becomes more regular (periodic). The same phenomena is true for the autocorrelation function. ● It is clear that the low order coefficients of the cepstrum contain information about the vocal tract, while the higher order coefficients contain primarily information about the excitation. (Actually, the higher order coefficients contain both types of information, but the frequency of periodicity dominates.) Hence, for speech signals, it seems the vocal tract response and the excitation signal can be separated using simple windowing in the quefrency domain. SOURCE-FILTER SEPARATION VIA THE CEPSTRUM An example of source-filter separation using voiced speech: (a) Windowed Signal (b) Log Spectrum (c) Filtered Cepstrum (n < N) (d) Smoothed Log Spectrum (e) Excitation Signal (f) Log Spectrum (high freq.) An example of source-filter separation using unvoiced speech: (a) Windowed Signal (b) Log Spectrum (c) Filtered Cepstrum (n < N) (d) Smoothed Log Spectrum The reason this works is simple: the fundamental frequency for the speaker produces a peak in the cepstrum sequence that is far removed (n > N) from the influence of the vocal tract (n < N). You can also demonstrate this using an autocorrelation function. What happens for an extremely high-pitched female or child? FREQUENCY WARPING USING ALL-PASS TRANSFORMATIONS Recall the bilinear transform: which implements a nonlinear warping of the frequency axis: This can be implemented as a series of all-pass transformations: The cepstral coefficients are input and the output are frequency-warped cepstral coefficients. This is an interesting way to implement speaker-specific warpings (e.g., male vs. female speakers). MEL-FREQUENCY CEPSTRUM Recall our filterbank, which we construct in mel-frequency domain using a triangularly-shaped weighting function applied to mel-transformed log-magnitude spectral samples: After computing the DFT, and the log magnitude spectrum (to obtain the real cepstrum), we compute the filterbank outputs, and then use a discrete cosine transform: to compute the mel-frequency cepstrum coefficients. Note that the triangular weighting functions are applied directly to the magnitude spectrum, and then the logarithm is taken after the spectral samples are averaged. The resulting coefficients are an approximation to the the cepstrum, and in reality simply represent an orthogonal and compact representation of the log magnitude spectrum. We typically use 24 filterbank samples at an 8 kHz sampling frequency, and truncate the DCT to 12 MFCC coefficients. Adding energy gives us a total of 13 coefficients for our base feature vector. LIFTERING: WINDOWING CEPSTRAL COEFFICIENTS ● Low order cepstral coefficients are sensitive to spectral slope, glottal pulse shape, etc. ● High order cepstral coefficients are sensitive to the analysis window position and other temporal artifacts. ● For speaker independent recognition, it is best to minimize such speaker-dependent variations in the features prior to recognition. ● We can reduce the variations in these coefficients by using a raised sine window that emphasizes coefficients at the center of the window: ● L is the number of cepstral